Skip to content

Latest commit

 

History

History
35 lines (24 loc) · 1.15 KB

README.md

File metadata and controls

35 lines (24 loc) · 1.15 KB
Raw

MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation (Accepted by WACV 2024)

The code is based on MMSegmentaion v0.24.1.

Installation

For install and data preparation, please refer to the guidelines in MMSegmentation.

pip install timm
cd MetaSeg
python setup.py develop

Training

Download backbone [ MSCAN-T & MSCAN-B ] pretrained weights.

Put them in a folder pretrain/.

Example - Train MetaSeg-T on ADE20K:

CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./tools/dist_train.sh local_configs/metaseg/tiny/metaseg.tiny.512x512.ade.160k.py <GPU_NUM>

Evaluation

Example - Evaluate MetaSeg-T on ADE20K:

# Single-gpu testing
CUDA_VISIBLE_DEVICES=0 python tools/test.py local_configs/metaseg/tiny/metaseg.tiny.512x512.ade.160k.py /path/to/checkpoint_file

# Multi-gpu testing
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./tools/dist_test.sh local_configs/metaseg/tiny/metaseg.tiny.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM>